Work

Stochastic Super-resolution for Gaussian Microtextures

Year: 2025

Type: article

Abstract: . Super-resolution (SR) is the problem that consists in reconstructing images that have been degraded by a zoom-out operator. This is an ill-posed problem that does not have a unique solution, and numerical approaches rely on a prior on high-resolution images. While optimization-based methods are generally deterministic, with the rise of image generative models more and more interest has been given to stochastic SR, that is, sampling among all possible SR images associated with a given low-resolution input. In this paper, we construct an efficient, stable, and provably exact sampler for the stochastic SR of Gaussian microtextures. Even though our approach is limited regarding the scope of images it encompasses, our algorithm is competitive with deep learning state-of-the-art methods both in terms of perceptual metric and execution time when applied to microtextures. The framework of Gaussian microtextures also allows us to rigorously discuss the limitations of various reconstruction metrics to evaluate the efficiency of SR routines. An implementation of our algorithm is available at https://github.com/emilePi/Stochastic_SR_for_Gaussian_textures .

Source: SIAM Journal on Imaging Sciences

DOI: 10.1137/24m1657407

Authors: Émile Pierret, Bruno Galerne

Institutions: Université de Tours, Université d'Orléans, Centre National de la Recherche Scientifique, Institut Denis Poisson, Institut Universitaire de France

Language: English

Topic: Advanced Image Processing Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Agence Nationale de la Recherche

Awards: Models, Inference and Synthesis for Texture In Color, G7619895121